Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

655 Scopus citations


Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware. However, their application in machine learning have largely been limited to very shallow neural network architectures for simple problems. In this paper, we propose a novel algorithmic technique for generating an SNN with a deep architecture, and demonstrate its effectiveness on complex visual recognition problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and Residual network architectures, with significantly better accuracy than the state-of-the-art. Finally, we present analysis of the sparse event-driven computations to demonstrate reduced hardware overhead when operating in the spiking domain.

Original languageEnglish (US)
Article number95
JournalFrontiers in Neuroscience
StatePublished - Mar 7 2019

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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